Summary

This mid-level data engineering role builds production-grade pipelines, data models and reusable data products for internal audit at Goldman Sachs. The stack includes Python, Pandas, Polars, PySpark, SQL, Snowflake, DB2 and PostgreSQL, with a focus on data quality, governance and lakehouse patterns. The role is office-based in Birmingham, United Kingdom.

Responsibilities

• Build and support production-grade etl pipelines
• Develop Python solutions with Pandas and PySpark
• Write advanced SQL for transformation and reconciliation
• Design dimensional and lakehouse data models
• Implement data quality checks and lineage
• Deliver governed semantic data products
• Provide production support and review code